Please use this identifier to cite or link to this item: http://dx.doi.org/10.25673/123531
Title: Digital technologies as enablers of organizational resilience in SMEs
Author(s): Menschulin, Daniel
Behrendt, Fabian
Issue Date: 2026
Type: Article
Language: English
Publisher: Otto von Guericke University Library, Magdeburg, Germany
URN: urn:nbn:de:gbv:ma9:1-1981185920-1254656
Subjects: Digital technologies
SMEs
Organizational resilience
Abstract: Small and medium-sized enterprises (SMEs) are particularly vulnerable to disruptions in supply chains because their limited financial, human, and organizational buffers make it difficult for them to absorb and offset shocks (Amin & Jolevski, 2023; Eggers, 2020; OECD, 2024). At the same time, digital technologies are increasingly being discussed as levers for strengthening organizational and supply chain resilience; however, the mechanisms of action often remain unclear and SME-specific conditions are under-examined (OECD, 2021). This paper therefore develops a mechanism-based conceptual model that explains how key technology classes, such as monitoring/IoT, data integration/platforms, analytics/AI, and digital collaboration, operate through three resilience mechanisms: (1) visibility and sensemaking, (2) decision quality and response speed, and (3) organizational learning and adaptation (Awad & Martín-Rojas, 2024; Duchek, 2020). Building on conceptualizations of resilience and current evidence regarding the role of information processing, transparency, and innovation capacity, measurable dimensions and indicator logics are proposed that account for SME realities such as data heterogeneity, skills shortages, and overcomplexity (Chen et al., 2021; OECD, 2021). The discussion derives testable proposals and demonstrates how digital twin and simulation approaches, as well as AI-supported decision aids, can enhance resilience in SME contexts not only through better decisions but also through structured learning loops (Pan et al., 2025; Roman et al., 2025; Weigert & Behrendt, 2022).
URI: https://opendata.uni-halle.de//handle/1981185920/125465
http://dx.doi.org/10.25673/123531
Open Access: Open access publication
License: (CC BY 4.0) Creative Commons Attribution 4.0(CC BY 4.0) Creative Commons Attribution 4.0
Related Resource (URI): https://opendata.uni-halle.de//handle/1981185920/125459
Appears in Collections:Fakultät für Maschinenbau (OA)

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